Anomaly-based intrusion detection system for IoT application
نویسندگان
چکیده
Abstract Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, many more. Those applications are increasingly popular because they address real-time problems. In contrast, use of transmission communication protocols raised serious security concerns for IoT devices, traditional methods signature rule-based inefficient securing these devices. Hence, identifying network traffic behavior mitigating cyber attacks important to provide guaranteed security. Therefore, we develop an Intrusion Detection System (IDS) based on deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) detect anomalies. The PCC-CNN combines features obtained from linear-based extractions followed by Network. It performs binary classification anomaly detection also multiclass types attacks. is evaluated three publicly available datasets: NSL-KDD, CICIDS-2017, IOTID20. We first train test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification Regression Tree,& Support Vector Machine) PCC-based Machine Learning models evaluate performance. achieve best similar accuracy KNN CART 98%, 99%, respectively, datasets. On other hand, promising performance with better 99.89% low misclassification rate 0.001 our proposed model. integrated promising, (or False alarm rate) 0.02, 0.00 Binary Multiclass intrusion classifiers. Finally, compare discuss comparison PCC-ML models. Our Deep (DL)-based IDS outperforms methods.
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ژورنال
عنوان ژورنال: Discover Internet of things
سال: 2023
ISSN: ['2730-7239']
DOI: https://doi.org/10.1007/s43926-023-00034-5